huiwang Ji is currently an Associate Professor in the Department of Computer Science & Engineering, Texas A&M University, leading the Data Integration, Visualization, and Exploration (DIVE) Laboratory. Ji received the Ph.D. degree in Computer Science from Arizona State University in 2010, advised by Prof. Jieping Ye. His research interests include machine learning, data mining, and computational neuroscience. Ji received the National Science Foundation CAREER Award in 2014. He has authored over 80 research articles and has coauthored a book. Currently, Ji serves as an Action Editor for Data Mining and Knowledge Discovery, and an Associate Editor for ACM Transactions on Knowledge Discovery from Data, IEEE Transactions on Neural Networks and Learning Systems, and BMC Bioinformatics. Ji is a Program Chair for the 2017 Bioimage Informatics Conference and a senior member of IEEE.
- Banerjee, D., Islam, K., Xue, K., Mei, G., Xiao, L., Zhang, G., ... Li, J. (2019). A deep transfer learning approach for improved post-traumatic stress disorder diagnosis. Knowledge and Information Systems.
- Feng, Y., Yang, F., Zhou, X., Guo, Y., Tang, F., Ren, F., Guo, J., & Ji, S. (2018). A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 1-1.
- Li, Z., Butler, E., Li, K., Lu, A., Ji, S., & Zhang, S. (2018). Large-scale Exploration of Neuronal Morphologies Using Deep Learning and Augmented Reality.. Neuroinformatics. 16(3-4), 339-349.
- Zhang, L., Zhao, Y., Zhu, Z., Shen, D., & Ji, S. (2018). Multi-View Missing Data Completion. IEEE Transactions on Knowledge and Data Engineering. 30(7), 1296-1309.
- Cai, L., Wu, B., & Ji, S. (2018). Neuronal Activities in the Mouse Visual Cortex Predict Patterns of Sensory Stimuli.. Neuroinformatics. 16(3-4), 473-488.
- Cai, L., Wang, Z., Gao, H., Shen, D., & Ji, S. (2018). Deep adversarial learning for multi-modality missing data completion. 1158-1166.
- Gao, H., Wang, Z., & Ji, S. (2018). Large-scale learnable graph convolutional networks. 1416-1424.
- Wang, Z., & Ji, S. (2018). Learning convolutional text representations for visual question answering. 594-602.
- Wang, Z., & Ji, S. (2018). Smoothed dilated convolutions for improved dense prediction. 2486-2495.
- Chen, Y., Shi, M., Gao, H., Shen, D., Cai, L., & Ji, S. (2018). Voxel deconvolutional networks for 3D brain image labeling. 1226-1234.